// Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

/******************************************************************************
 * Copyright (c) 2024, Jay Shah, Ganesh Bikshandi, Ying Zhang, Vijay Thakkar, Pradeep Ramani, Tri Dao.
 ******************************************************************************/

#pragma once

#include <cutlass/cutlass.h>
#include <cutlass/array.h>
#include <cutlass/numeric_types.h>
#include <cutlass/numeric_conversion.h>
#include "cutlass/pipeline/pipeline.hpp"

#include "cute/tensor.hpp"

#include "cutlass/gemm/collective/collective_builder.hpp"

using namespace cute;

enum class AttnNamedBarriers {
    QueryEmpty = 0,
    ValueEmpty = 1,
    TileCountSmemEmpty = 2,
    TileCountSmemFull = 3,
    WarpSchedulerWG1 = 4,
    WarpSchedulerWG2 = 5,
    WarpSchedulerWG3 = 6,
};



template <typename Ktraits>
struct CollectiveMainloopAttn {

    using Element = typename Ktraits::Element;
    using TileShape_MNK = typename Ktraits::TileShape_MNK;
    using ClusterShape = typename Ktraits::ClusterShape_MNK;

    static constexpr int kStages = Ktraits::kStages;
    static constexpr int kHeadDim = Ktraits::kHeadDim;
    static constexpr int kBlockM = Ktraits::kBlockM;
    static constexpr int kBlockN = Ktraits::kBlockN;

    using ShapeT = cute::Shape<int32_t, int32_t, int32_t>;
    using StrideT = cute::Shape<int32_t, _1, int32_t>;
    using LayoutT = cute::Layout<ShapeT, StrideT>;


    using GmemTiledCopyQ = cute::SM90_TMA_LOAD;
    using GmemTiledCopyKV = decltype(cutlass::gemm::collective::detail::sm90_cluster_shape_to_tma_atom(shape<0>(ClusterShape{})));
    using GmemTiledCopyO = typename Ktraits::GmemTiledCopyO;

    using SmemLayoutAtomQ = decltype(cutlass::gemm::collective::detail::ss_smem_selector<GMMA::Major::K, Element,
        decltype(cute::get<0>(TileShape_MNK{})), decltype(cute::get<2>(TileShape_MNK{}))>());
    using SmemLayoutQ = decltype(tile_to_shape(SmemLayoutAtomQ{}, select<0, 2>(TileShape_MNK{})));

    using SmemLayoutAtomK = decltype(cutlass::gemm::collective::detail::ss_smem_selector<GMMA::Major::K, Element,
        decltype(cute::get<1>(TileShape_MNK{})), decltype(cute::get<2>(TileShape_MNK{}))>());
    using SmemLayoutK =
        decltype(tile_to_shape(SmemLayoutAtomK{},
                 make_shape(shape<1>(TileShape_MNK{}), shape<2>(TileShape_MNK{}), Int<kStages>{})));
    using SmemLayoutV = SmemLayoutK;
    // Note this is the transpose in terms of the view, not in terms of memory.
    using SmemLayoutVt =
        decltype(cute::composition(SmemLayoutV{},
                                   make_layout(make_shape(get<2>(TileShape_MNK{}), get<1>(TileShape_MNK{}), Int<kStages>{}),
                                               make_stride(get<1>(TileShape_MNK{}), _1{}, Int<size(SmemLayoutV{}(_, _, _0{}))>{}))));
    using SmemLayoutO = typename Ktraits::SmemLayoutO;
    using SmemCopyAtomO = typename Ktraits::SmemCopyAtomO;

    using TMA_Q = decltype(make_tma_copy(
        GmemTiledCopyQ{},
        make_tensor(
            make_gmem_ptr(static_cast<Element const*>(nullptr)),
            repeat_like(StrideT{}, int32_t(0)),
            StrideT{}
        ),
        SmemLayoutQ{},
        select<0, 2>(TileShape_MNK{}),
        _1{}));  // no mcast for Q

    using TMA_KV = decltype(make_tma_copy(
        GmemTiledCopyKV{},
        make_tensor(
            make_gmem_ptr(static_cast<Element const*>(nullptr)),
            repeat_like(StrideT{}, int32_t(0)),
            StrideT{}
        ),
        take<0, 2>(SmemLayoutK{}),
        select<1, 2>(TileShape_MNK{}),
        size<0>(ClusterShape{}))); // mcast along M mode for this N load, if any

    static constexpr int NumMmaThreads = size(typename Ktraits::TiledMma0{});
    using MainloopPipeline = typename Ktraits::MainloopPipeline;
    using PipelineParams = typename MainloopPipeline::Params;
    using PipelineState = typename MainloopPipeline::PipelineState;

    // Set the bytes transferred in this TMA transaction (may involve multiple issues)
    static constexpr uint32_t TmaTransactionBytesQ = static_cast<uint32_t>(size(SmemLayoutQ{}) * cutlass::sizeof_bits_v<Element> / 8);
    static constexpr uint32_t TmaTransactionBytesK = static_cast<uint32_t>(size(take<0, 2>(SmemLayoutK{})) * cutlass::sizeof_bits_v<Element> / 8);

    static constexpr bool UseSchedulerBarrier = kHeadDim <= 128;

    // Host side kernel arguments
    struct Arguments {
        Element const* ptr_Q;
        LayoutT layout_Q;
        Element const* ptr_K;
        LayoutT layout_K;
        Element const* ptr_V;
        LayoutT layout_V;
        float const softmax_scale_log2;
    };

    // Device side kernel params
    struct Params {
        LayoutT layout_Q;
        LayoutT layout_K;
        LayoutT layout_V;
        cutlass::FastDivmod qhead_per_khead_divmod;
        TMA_Q tma_load_Q;
        TMA_KV tma_load_K, tma_load_V;
        float const softmax_scale_log2;
    };


    static Params
    to_underlying_arguments(Arguments const& args) {
        Tensor mQ = make_tensor(make_gmem_ptr(args.ptr_Q), args.layout_Q);
        TMA_Q tma_load_Q = make_tma_copy(
            GmemTiledCopyQ{},
            mQ,
            SmemLayoutQ{},
            select<0, 2>(TileShape_MNK{}),
            _1{}); // no mcast for Q
        Tensor mK = make_tensor(make_gmem_ptr(args.ptr_K), args.layout_K);
        TMA_KV tma_load_K = make_tma_copy(
            GmemTiledCopyKV{},
            mK,
            SmemLayoutK{}(_, _, _0{}),
            select<1, 2>(TileShape_MNK{}),
            size<0>(ClusterShape{})); // mcast along M mode for this N load, if any
        Tensor mV = make_tensor(make_gmem_ptr(args.ptr_V), args.layout_V);
        TMA_KV tma_load_V = make_tma_copy(
            GmemTiledCopyKV{},
            mV,
            SmemLayoutV{}(_, _, _0{}),
            select<1, 2>(TileShape_MNK{}),
            size<0>(ClusterShape{})); // mcast along M mode for this N load, if any
        return {args.layout_Q, args.layout_K, args.layout_V,
                cutlass::FastDivmod(cute::ceil_div(get<2>(args.layout_Q.shape()), get<2>(args.layout_K.shape()))),
                tma_load_Q, tma_load_K, tma_load_V,
                args.softmax_scale_log2};
    }

    /// Issue Tma Descriptor Prefetch -- ideally from a single thread for best performance
    CUTLASS_DEVICE
    static void prefetch_tma_descriptors(Params const& mainloop_params) {
        cute::prefetch_tma_descriptor(mainloop_params.tma_load_Q.get_tma_descriptor());
        cute::prefetch_tma_descriptor(mainloop_params.tma_load_K.get_tma_descriptor());
        cute::prefetch_tma_descriptor(mainloop_params.tma_load_V.get_tma_descriptor());
    }

    template <typename MTensor, typename Shape>
    CUTLASS_DEVICE auto get_local_tile_tensor(
        const MTensor &m_tensor,
        const Shape &tile_shape,
        const int *cu_seq_len,
        const int bidh,
        const int bidb,
        const int actual_seq_len) const {
        auto g_offset = local_tile(
            m_tensor(_, _, bidh),
            cute::make_shape(1, get<1>(tile_shape)),
            make_coord(cu_seq_len[bidb], _0{}));
        auto g_sequence = make_tensor(
            g_offset.data(),
            make_layout(
                cute::make_shape(actual_seq_len, get<1>(tile_shape)),
                g_offset.stride()
            ));
        auto g_tensor = local_tile(g_sequence, tile_shape, make_coord(_, _0{}));
        return g_tensor;
    }


    template <bool UseMoba, typename SharedStorage>
    CUTLASS_DEVICE void
    load(Params const& mainloop_params,
         MainloopPipeline pipeline_k,
         MainloopPipeline pipeline_v,
         PipelineState& smem_pipe_write_k,
         PipelineState& smem_pipe_write_v,
         SharedStorage &shared_storage,
         const int *qk_gate_topk_idx,
         const int n_block_max,
         const int m_block,
         const int bidh,
         const int bidb,
         const int *cu_seq_q,
         const int *cu_seq_k,
         const int seq_len_q,
         const int seq_len_k) {

        Tensor sQ = make_tensor(make_smem_ptr(shared_storage.smem_q.data()), SmemLayoutQ{});
        Tensor sK = make_tensor(make_smem_ptr(shared_storage.smem_k.data()), SmemLayoutK{});
        Tensor sV = make_tensor(make_smem_ptr(shared_storage.smem_v.data()), SmemLayoutV{});

        Tensor mQ = mainloop_params.tma_load_Q.get_tma_tensor(mainloop_params.layout_Q.shape());
        Tensor mK = mainloop_params.tma_load_K.get_tma_tensor(mainloop_params.layout_K.shape());
        Tensor mV = mainloop_params.tma_load_V.get_tma_tensor(mainloop_params.layout_V.shape());
        int bidh_kv = mainloop_params.qhead_per_khead_divmod.divide(bidh);

        Tensor gQ = get_local_tile_tensor(
            mQ, select<0, 2>(TileShape_MNK{}), cu_seq_q, bidh, bidb, seq_len_q)(_, _, m_block);
        Tensor gK = get_local_tile_tensor(
            mK, select<1, 2>(TileShape_MNK{}), cu_seq_k, bidh_kv, bidb, seq_len_k);
        Tensor gV = get_local_tile_tensor(
            mV, select<1, 2>(TileShape_MNK{}), cu_seq_k, bidh_kv, bidb, seq_len_k);

        Tensor sQ_x = make_tensor(sQ.data(), make_layout(sQ.layout(), Layout<_1>{}));
        Tensor gQ_x = make_tensor(gQ.data(), make_layout(gQ.layout(), Layout<_1>{}));
        auto [tQgQ, tQsQ] = tma_partition(mainloop_params.tma_load_Q, _0{}, Layout<_1>{},group_modes<0, 2>(sQ_x), group_modes<0, 2>(gQ_x));
        auto [tKgK, tKsK] = tma_partition(mainloop_params.tma_load_K, _0{}, Layout<_1>{},group_modes<0, 2>(sK), group_modes<0, 2>(gK));
        auto [tVgV, tVsV] = tma_partition(mainloop_params.tma_load_V, _0{}, Layout<_1>{},group_modes<0, 2>(sV), group_modes<0, 2>(gV));

        uint16_t mcast_mask_kv = 0;

        int n_block = n_block_max - 1;

        int lane_predicate = cute::elect_one_sync();

        if (lane_predicate) {
            shared_storage.barrier_Q.arrive_and_expect_tx(TmaTransactionBytesQ);
            copy(mainloop_params.tma_load_Q.with(reinterpret_cast<cutlass::arch::ClusterTransactionBarrier::ValueType&>(shared_storage.barrier_Q), 0 /*mcast_mask*/), tQgQ, tQsQ);
        }


        if (lane_predicate) {
            pipeline_k.producer_acquire(smem_pipe_write_k);
            copy(mainloop_params.tma_load_K.with(*pipeline_k.producer_get_barrier(smem_pipe_write_k), mcast_mask_kv), tKgK(_, n_block), tKsK(_, smem_pipe_write_k.index()));
            ++smem_pipe_write_k;
        }

        if (lane_predicate) {
            int idx = 0;
            #pragma unroll 2
            for (; n_block > 0; ) {
                pipeline_k.producer_acquire(smem_pipe_write_k);
                int pre_idx = 1;
                if constexpr (UseMoba) {
                    pre_idx = qk_gate_topk_idx[idx];
                }
                copy(mainloop_params.tma_load_K.with(*pipeline_k.producer_get_barrier(smem_pipe_write_k), mcast_mask_kv), tKgK(_, n_block - pre_idx), tKsK(_, smem_pipe_write_k.index()));

                ++smem_pipe_write_k;
                pipeline_v.producer_acquire(smem_pipe_write_v);
                copy(mainloop_params.tma_load_V.with(*pipeline_v.producer_get_barrier(smem_pipe_write_v), mcast_mask_kv), tVgV(_, n_block), tVsV(_, smem_pipe_write_v.index()));
                ++smem_pipe_write_v;
                n_block -= pre_idx;
                idx += 1;
            }
        }
        if (lane_predicate) {
            pipeline_v.producer_acquire(smem_pipe_write_v);
            copy(mainloop_params.tma_load_V.with(*pipeline_v.producer_get_barrier(smem_pipe_write_v), mcast_mask_kv), tVgV(_, n_block), tVsV(_, smem_pipe_write_v.index()));
            ++smem_pipe_write_v;
        }
    }

    CUTLASS_DEVICE void
    warp_scheduler_barrier_sync() {
        if constexpr (UseSchedulerBarrier) {
            cutlass::arch::NamedBarrier::sync(NumMmaThreads, static_cast<int>(AttnNamedBarriers::WarpSchedulerWG1) - 1 + cutlass::canonical_warp_group_idx() /*id*/);
        }
    }

    CUTLASS_DEVICE void
    mma_init() {
        if constexpr (!UseSchedulerBarrier) { return; }
        static_assert(NumMmaThreads == 2 * cutlass::NumThreadsPerWarpGroup || NumMmaThreads == 3 * cutlass::NumThreadsPerWarpGroup);
        if (cutlass::canonical_warp_group_idx() > 1) {
            cutlass::arch::NamedBarrier::arrive(NumMmaThreads, static_cast<int>(AttnNamedBarriers::WarpSchedulerWG1) - 1 + 1 /*id*/);
        }
        if constexpr (NumMmaThreads == 3 * cutlass::NumThreadsPerWarpGroup) {
            if (cutlass::canonical_warp_group_idx() > 2) {
                cutlass::arch::NamedBarrier::arrive(NumMmaThreads, static_cast<int>(AttnNamedBarriers::WarpSchedulerWG1) - 1 + 2 /*id*/);
            }
        }

    }

    CUTLASS_DEVICE void
    warp_scheduler_barrier_arrive() {
        if constexpr (!UseSchedulerBarrier) { return; }
        static_assert(NumMmaThreads == 2 * cutlass::NumThreadsPerWarpGroup || NumMmaThreads == 3 * cutlass::NumThreadsPerWarpGroup);
        if constexpr (NumMmaThreads == 2 * cutlass::NumThreadsPerWarpGroup) {
            cutlass::arch::NamedBarrier::arrive(NumMmaThreads, static_cast<int>(AttnNamedBarriers::WarpSchedulerWG1) - 1 + (3 - cutlass::canonical_warp_group_idx()) /*id*/);
        } else {
            cutlass::arch::NamedBarrier::arrive(NumMmaThreads, static_cast<int>(AttnNamedBarriers::WarpSchedulerWG1) - 1 + (cutlass::canonical_warp_group_idx() <= 2 ? cutlass::canonical_warp_group_idx() + 1 : cutlass::canonical_warp_group_idx() + 1 - 3)  /*id*/);
            cutlass::arch::NamedBarrier::arrive(NumMmaThreads, static_cast<int>(AttnNamedBarriers::WarpSchedulerWG1) - 1 + (cutlass::canonical_warp_group_idx() <= 1 ? cutlass::canonical_warp_group_idx() + 2 : cutlass::canonical_warp_group_idx() + 2 - 3)  /*id*/);
        }
    }


    template <bool UseMoba, typename SharedStorage, typename FrgTensorO, typename Softmax>
    CUTLASS_DEVICE void
    mma(Params const& mainloop_params,
        MainloopPipeline pipeline_k,
        MainloopPipeline pipeline_v,
        PipelineState& smem_pipe_read_k,
        PipelineState& smem_pipe_read_v,
        FrgTensorO& tOrO,
        Softmax& softmax,
        const int *qk_gate_topk_idx,
        const int n_block_max,
        const int thread_idx,
        const int m_block,
        const int seq_len_q,
        const int seq_len_k,
        SharedStorage& shared_storage) {

        Tensor sQ = make_tensor(make_smem_ptr(shared_storage.smem_q.data()), SmemLayoutQ{});
        Tensor sK = make_tensor(make_smem_ptr(shared_storage.smem_k.data()), SmemLayoutK{});
        Tensor sVt = make_tensor(make_smem_ptr(shared_storage.smem_v.data()), SmemLayoutVt{});

        typename Ktraits::TiledMma0 tiled_mma0;
        typename Ktraits::TiledMma1 tiled_mma1;
        auto threadMma0 = tiled_mma0.get_thread_slice(thread_idx);
        auto threadMma1 = tiled_mma1.get_thread_slice(thread_idx);

        Tensor tSrQ = threadMma0.partition_fragment_A(sQ);
        Tensor tSrK = threadMma0.partition_fragment_B(sK);
        Tensor tOrV = threadMma1.partition_fragment_B(sVt);

        auto consumer_wait = [](auto& pipeline, auto& smem_pipe_read) {
            auto barrier_token = pipeline.consumer_try_wait(smem_pipe_read);
            pipeline.consumer_wait(smem_pipe_read, barrier_token);
        };

        tiled_mma1.accumulate_ = GMMA::ScaleOut::Zero;

        int n_block = n_block_max - 1;

        cutlass::ConsumerToken barrier_token = static_cast<cutlass::BarrierStatus>(shared_storage.barrier_Q.try_wait(0));
        if (barrier_token == cutlass::BarrierStatus::WaitAgain) { shared_storage.barrier_Q.wait(0); }

        Tensor tSrS = partition_fragment_C(tiled_mma0, select<0, 1>(TileShape_MNK{}));
        consumer_wait(pipeline_k, smem_pipe_read_k);
        warp_scheduler_barrier_sync();
        gemm</*zero_init=*/true, /*wg_wait=*/-1>(tiled_mma0, tSrQ, tSrK(_, _, _, smem_pipe_read_k.index()), tSrS);
        warp_scheduler_barrier_arrive();
        warpgroup_wait<0>();
        pipeline_k.consumer_release(smem_pipe_read_k);
        ++smem_pipe_read_k;

        auto col_limit_causal = [&](int row, int n_block) {
            return row + 1 + seq_len_k - n_block * kBlockN - seq_len_q + m_block * kBlockM;
        };
        Tensor cS = cute::make_identity_tensor(select<0, 1>(TileShape_MNK{}));
        Tensor tScS = threadMma0.partition_C(cS);
        #pragma unroll
        for (int i = 0; i < size(tSrS); ++i) {
            if (int(get<1>(tScS(i))) >=
                std::min(seq_len_k - n_block * kBlockN, col_limit_causal(int(get<0>(tScS(i))), n_block))) {
                tSrS(i) = -INFINITY;
            }
        }

        softmax.template online_softmax</*Is_first=*/true>(tSrS, mainloop_params.softmax_scale_log2);

        Tensor tOrP = make_tensor(convert_type<Element>(tSrS).data(), convert_layout_acc_Aregs<typename Ktraits::TiledMma1>(tSrS.layout()));
        Tensor scores_scale = make_fragment_like(softmax.row_max);
        clear(scores_scale);

        int idx = 0;
        #pragma unroll 2
        for (; n_block > 0; ) {
            Tensor tSrS = partition_fragment_C(tiled_mma0, select<0, 1>(TileShape_MNK{}));
            consumer_wait(pipeline_k, smem_pipe_read_k);
            warp_scheduler_barrier_sync();
            gemm</*zero_init=*/true, /*wg_wait=*/-1>(tiled_mma0, tSrQ, tSrK(_, _, _, smem_pipe_read_k.index()), tSrS);
            softmax.rescale_o(tOrO, scores_scale);
            consumer_wait(pipeline_v, smem_pipe_read_v);
            gemm</*zero_init=*/false, /*wg_wait=*/-1>(tiled_mma1, tOrP, tOrV(_, _, _, smem_pipe_read_v.index()), tOrO);
            warp_scheduler_barrier_arrive();
            warpgroup_wait<1>();
            pipeline_k.consumer_release(smem_pipe_read_k);  // release K
            cute::copy(softmax.template max</*Is_first=*/false>(tSrS, mainloop_params.softmax_scale_log2), scores_scale);
            softmax.template online_softmax</*Is_first=*/false>(tSrS, mainloop_params.softmax_scale_log2);
            warpgroup_wait<0>();
            pipeline_v.consumer_release(smem_pipe_read_v);  // release V
            ++smem_pipe_read_k;
            ++smem_pipe_read_v;
            cute::copy(make_tensor(convert_type<Element>(tSrS).data(), convert_layout_acc_Aregs<typename Ktraits::TiledMma1>(tSrS.layout())), tOrP);
            if constexpr (UseMoba) {
                n_block -= qk_gate_topk_idx[idx];
                idx += 1;
            } else {
                n_block -= 1;
            }
        }

        softmax.rescale_o(tOrO, scores_scale);
        consumer_wait(pipeline_v, smem_pipe_read_v);
        gemm</*zero_init=*/false, /*wg_wait=*/-1>(tiled_mma1, tOrP, tOrV(_, _, _, smem_pipe_read_v.index()), tOrO);
        cute::copy(softmax.finalize(mainloop_params.softmax_scale_log2), scores_scale);
        warpgroup_wait<0>();
        pipeline_v.consumer_release(smem_pipe_read_v);
        ++smem_pipe_read_v;

        softmax.rescale_o(tOrO, scores_scale);
    }

    template <int NumMmaThreads, typename SharedStorage, typename FrgTensorO, typename TiledMma, typename T>
    CUTLASS_DEVICE void
    store(Params const& mainloop_params,
        FrgTensorO const& tOrO,
        SharedStorage& shared_storage,
        TiledMma tiled_mma,
        int thread_idx,
        const int o_head_stride,
        const int real_seq,
        T * out_ptr) {

        Tensor sO = make_tensor(make_smem_ptr(shared_storage.smem_o.data()), SmemLayoutO{});
        auto smem_tiled_copy_O = make_tiled_copy_C(SmemCopyAtomO{}, tiled_mma);
        auto smem_thr_copy_O = smem_tiled_copy_O.get_thread_slice(thread_idx);

        Tensor tOrO_out = convert_type<Element>(tOrO);
        Tensor taccOrO = smem_thr_copy_O.retile_S(tOrO_out);
        Tensor taccOsO = smem_thr_copy_O.partition_D(sO);

        cutlass::arch::NamedBarrier::sync(NumMmaThreads, static_cast<int>(AttnNamedBarriers::ValueEmpty) /*id*/);
        cute::copy(smem_tiled_copy_O, taccOrO, taccOsO);
        cutlass::arch::fence_view_async_shared(); // ensure smem writes are visible to TMA
        cutlass::arch::NamedBarrier::arrive(NumMmaThreads + cutlass::NumThreadsPerWarp,cutlass::arch::ReservedNamedBarriers::EpilogueBarrier);

        Tensor gO = make_tensor(make_gmem_ptr(out_ptr),
            Shape<Int<kBlockM>, Int<kHeadDim>>{},
            make_stride(o_head_stride, _1{}));

        GmemTiledCopyO gmem_tiled_copy_O;
        auto gmem_thr_copy_O = gmem_tiled_copy_O.get_thread_slice(thread_idx);

        Tensor tOsO = gmem_thr_copy_O.partition_S(sO);
        Tensor tOgO = gmem_thr_copy_O.partition_D(gO);

        Tensor cO = make_identity_tensor(Shape<Int<kBlockM>, Int<kHeadDim>>{});

        Tensor tOcO = gmem_thr_copy_O.partition_S(cO);

        if (real_seq >= kBlockM) {
            copy<true>(gmem_tiled_copy_O, tOsO, tOgO, tOcO);
        } else {
            copy<false>(gmem_tiled_copy_O, tOsO, tOgO, tOcO, real_seq);
        }
    }

};
